Optimization of Shape of Convergent-Divergent Nozzles by Using Improved Simple Genetic Algorithms

Abstract

Genetic Algorithms (GAs) have initiated a new scheme for solution of the complicated optimization problems. Here a genetic algorithm is developed for optimization of the convergent-divergent nozzles. First, a popular numerical method is chosen for solving the fluid dynamic problem. Then, optimization criterion and evaluation parameters (such as maximum thrust force) are determined. After introduction of the simple genetic algorithm, parameters of this method such as crossover probability, mutation probability and population size in each generation are used for optimization of the algorithm. After independent verification of the computational fluid dynamic scheme and the genetic algorithm, these two are integrated, and using different values for the above parameters, performance of the optimization algorithm for shape optimization of a nozzle is studied. Finally an optimum range of GA parameters are found, and are used to find optimum shape by less than 0.1% exhaustive search CPU time.